Beneficial effects of vaccenic acid on postprandial lipid metabolism and dyslipidemia: Impact of natural <i>trans</i>‐fats to improve CVD risk
Bibliographic record
Abstract
Abstract Abnormal non‐fasting (postprandial) lipid metabolism has been recognized as a significant contributor to dyslipidemia and cardiovascular disease (CVD) risk. Clinically, impaired metabolism of lipoproteins following a meal (e.g. chylomicrons) has been demonstrated in a number of chronic diseases, including obesity, insulin resistance, as well as type 1 and 2 diabetes. Given the proposed effects of dietary trans fat to contribute to a lipid profile that increases CVD risk, there has been a public health campaign in many countries to eliminate these fatty acids from the food supply. In contrast, our group has recently reported novel lipid‐lowering benefits of a major naturally‐occurring trans fatty acid vaccenic acid (VA, shorthand lipid name 18:1 trans‐11), in an animal model of dyslipidemia and the metabolic syndrome. Studies to date have shown that dietary supplementation of VA effectively reduces not only fasting lipids, but also postprandial triacylglycerol and chylomicron concentrations in obese JCR:LA‐cp rats. Evidence from animal studies to date suggest that VA may down‐regulate hepatic fatty acid synthesis and directly influence lipogenesis in the intestine. The discovery of new bioactive properties of VA is supported by clinical studies which have provided increased momentum for industry applications. In this review we summarize the emerging beneficial view of natural trans fats that have distinct and differential properties compared to those synthetically produced in partially hydrogenated vegetable oils (PHVO), with a particular focus on fasting and postprandial lipid metabolism in CVD risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".